{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b760a0f6",
   "metadata": {},
   "source": [
    "# Generalized Linear Models\n",
    "## Poisson Regression\n",
    "\n",
    "All three **Generalized Linear Models (GLM)** currently available in scikit-learn have been integrated for FHE in Concrete ML: PoissonRegressor, GammaRegressor and TweedieRegressor. Choosing the best GLM directly depends on the initial problem one has to solve, and more specifically, how the target values are distributed.  Detailed information about those models can be found in their associated [documentation](https://scikit-learn.org/stable/modules/classes.html#:~:text=Generalized%20linear%20models%20(GLM)) as well as their [user guide](https://scikit-learn.org/stable/modules/linear_model.html#:~:text=vs%20other%20solvers%E2%80%9D-,1.1.12.%20Generalized%20Linear%20Regression).\n",
    "\n",
    "This tutorial shows how to train **PoissonRegressor** and then run it in FHE using Concrete ML. We make use of quantization as FHE computations must be only be done on integers and show how this process does not degrade its performances.\n",
    "\n",
    "A more general comparison of all three models is available in the [GLM comparison notebook](GLMComparison.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "253288cf",
   "metadata": {},
   "source": [
    "### Import libraries\n",
    "\n",
    "We import scikit-learn libraries, Concrete quantization tools as well as the Poisson regression model from both scikit-learn and Concrete ML:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6200ab62",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "import numpy as np\n",
    "import sklearn\n",
    "from sklearn.datasets import fetch_openml\n",
    "from sklearn.linear_model import PoissonRegressor as SklearnPoissonRegressor\n",
    "from sklearn.metrics import mean_poisson_deviance\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from concrete.ml.sklearn import PoissonRegressor as ConcretePoissonRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f43e2387",
   "metadata": {},
   "source": [
    "And, finally, we import some helpers for visualization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d104c8df",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53e676b8",
   "metadata": {},
   "source": [
    "### Insurance claims data-set\n",
    "\n",
    "A Poisson regression is adapted for modeling count data as well as relative frequencies (by scaling the values). It therefore expects that the target values are non-negative and assumes that they follow a [Poisson distribution](https://en.wikipedia.org/wiki/Poisson_distribution).\n",
    "\n",
    "Thus, let's build a regression model that predicts the frequency of incidents in an insurance setting.\n",
    "\n",
    "We first download a [data-set](https://www.openml.org/d/41214) from OpenML that contains 670,000 examples giving the frequency of car accidents for drivers of various ages, past accident history, car type, car color, geographical region, etc. We only consider the first 50,000 examples in order to speed up the training part.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d451e829",
   "metadata": {},
   "outputs": [],
   "source": [
    "df, _ = fetch_openml(\n",
    "    data_id=41214, as_frame=True, cache=True, data_home=\"~/.cache/sklearn\", return_X_y=True\n",
    ")\n",
    "df = df.head(50000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39a70df7",
   "metadata": {},
   "source": [
    "The target variable is the number of claims per year, which is computed by the following formula :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5e163891",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Frequency\"] = df[\"ClaimNb\"] / df[\"Exposure\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75f4fdb7",
   "metadata": {},
   "source": [
    "Let's visualize our data-set, showing that the target variable \"Frequency\" has a Poisson distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2a124a62",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.ioff()\n",
    "fig, ax = plt.subplots(1, 2, figsize=(15, 7))\n",
    "fig.patch.set_facecolor(\"white\")\n",
    "ax[0].set_title(\"Frequency of claims vs. Driver Age\")\n",
    "ax[0].set_xlabel(\"Driver Age\")\n",
    "ax[0].set_ylabel(\"Frequency of claims\")\n",
    "ax[0].scatter(df[\"DrivAge\"], df[\"Frequency\"], marker=\"o\", color=\"#ffb700\")\n",
    "ax[1].set_title(\"Histogram of Frequency of claims\")\n",
    "ax[1].set_xlabel(\"Frequency of claims\")\n",
    "ax[1].set_ylabel(\"Count\")\n",
    "df[\"Frequency\"].hist(bins=30, log=True, ax=ax[1], color=\"black\")\n",
    "display(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c8310ab",
   "metadata": {},
   "source": [
    "We then split the data into a training and test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d81db277",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train, df_test = train_test_split(df, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4690cc15",
   "metadata": {},
   "source": [
    "## Simple single variable insurance incident frequency predictor\n",
    "\n",
    "Our first example only considers a single predictor feature. It is therefore easy to visualize the results. \n",
    "\n",
    "Let's extract our train and test data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "10fe1cad",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = df_train[\"DrivAge\"].values.reshape(-1, 1).astype(np.float64)\n",
    "test_data = np.sort(df_test[\"DrivAge\"].values).reshape(-1, 1).astype(np.float64)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faa5247c",
   "metadata": {},
   "source": [
    "We first train the scikit-learn PoissonRegressor model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "682fb2d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "sklearn_pr = SklearnPoissonRegressor(max_iter=300)\n",
    "sklearn_pr.fit(train_data, df_train[\"Frequency\"], sample_weight=df_train[\"Exposure\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "084fb296",
   "metadata": {},
   "source": [
    "We can now test this predictor on the test data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4953b03e",
   "metadata": {},
   "outputs": [],
   "source": [
    "sklearn_predictions = sklearn_pr.predict(test_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f28155cf",
   "metadata": {},
   "source": [
    "Now, let's visualize our predictions to see how our model performs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "111574ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "fig, ax = plt.subplots(1, figsize=(12, 8))\n",
    "fig.patch.set_facecolor(\"white\")\n",
    "ax.plot(test_data, sklearn_predictions, color=\"black\", label=\"Float clear trend line\")\n",
    "ax.scatter(df_test[\"DrivAge\"], df_test[\"Frequency\"], marker=\"o\", color=\"#ffb700\")\n",
    "ax.set_xlabel(\"Driver Age\")\n",
    "ax.set_ylim(0, 10)\n",
    "ax.set_title(\"Regression with sklearn\")\n",
    "ax.set_ylabel(\"Frequency of claims\")\n",
    "ax.legend(loc=\"upper right\")\n",
    "display(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "429d8cc8",
   "metadata": {},
   "source": [
    "### Analysis\n",
    "\n",
    "We will now convert it to FHE in order to visually compare some details about quantization. A multivariate model will then be considered afterwards."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d959640",
   "metadata": {},
   "source": [
    "### Let's visualize the performance of a **quantized Poisson regressor**\n",
    "\n",
    "First, we train the concrete PoissonRegressor model that will quantize the inputs and weights using 8 bits:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6d4dbb92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PoissonRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;PoissonRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>PoissonRegressor()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "PoissonRegressor()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concrete_pr = ConcretePoissonRegressor(n_bits=8)\n",
    "concrete_pr.fit(train_data, df_train[\"Frequency\"], sample_weight=df_train[\"Exposure\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab82ae87",
   "metadata": {},
   "source": [
    "We can now test this predictor on the test data the same way we did with scikit-learn:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9358b29b",
   "metadata": {},
   "outputs": [],
   "source": [
    "concrete_predictions = concrete_pr.predict(test_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5a50eb8",
   "metadata": {},
   "source": [
    "Finally, let's compare the Concrete quantized model's results with the scikit-learn one by measuring their goodness of fit on the test data using the Poisson deviance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "04777aeb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean Poisson deviance (scikit-learn): 1.6743\n",
      "mean Poisson deviance (Concrete ML): 1.6743\n"
     ]
    }
   ],
   "source": [
    "y_true = df_test[\"Frequency\"]\n",
    "sample_weight = df_test[\"Exposure\"]\n",
    "\n",
    "sklearn_score = mean_poisson_deviance(y_true, sklearn_predictions, sample_weight=sample_weight)\n",
    "concrete_score = mean_poisson_deviance(y_true, concrete_predictions, sample_weight=sample_weight)\n",
    "\n",
    "print(f\"mean Poisson deviance (scikit-learn): {sklearn_score:.4f}\")\n",
    "print(f\"mean Poisson deviance (Concrete ML): {concrete_score:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fb57c4a",
   "metadata": {},
   "source": [
    "We can observe that the deviances are identical (with an order of magnitude of about 1e-4). \n",
    " \n",
    "We then plot the two trend lines (scikit-learn float clear model and Concrete ML quantized clear model) to visualize this similarity. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5fb15eb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "fig, ax = plt.subplots(1, figsize=(12, 8))\n",
    "fig.patch.set_facecolor(\"white\")\n",
    "\n",
    "# Plot the scikit-learn in clear model's main trend line\n",
    "ax.plot(\n",
    "    test_data,\n",
    "    sklearn_predictions,\n",
    "    color=\"black\",\n",
    "    label=f\"scikit-learn float, d={sklearn_score:.3f}\",\n",
    ")\n",
    "\n",
    "# Plot the Concrete quantized in clear model's main trend line\n",
    "ax.plot(\n",
    "    test_data,\n",
    "    concrete_predictions,\n",
    "    color=\"red\",\n",
    "    label=f\"Concrete ML quantized, d={concrete_score:.3f}\",\n",
    ")\n",
    "\n",
    "# Plot the test data\n",
    "ax.scatter(df_test[\"DrivAge\"], df_test[\"Frequency\"], marker=\"o\", color=\"gray\", label=\"Test data\")\n",
    "\n",
    "# Parametrize the main figure\n",
    "ax.set_xlabel(\"Driver Age\")\n",
    "ax.set_ylim(0, 10)\n",
    "ax.set_title(\"Poisson Regression, float in clear and quantized in clear trend lines\")\n",
    "ax.set_ylabel(\"Frequency of claims\")\n",
    "ax.legend(loc=\"upper left\")\n",
    "ax.grid()\n",
    "\n",
    "\n",
    "# Set a zoomed-in figure\n",
    "axins = ax.inset_axes([0.5, 0.5, 0.47, 0.47])\n",
    "\n",
    "# Plot the scikit-learn in clear model's zoomed trend line\n",
    "axins.plot(\n",
    "    test_data,\n",
    "    sklearn_predictions,\n",
    "    color=\"black\",\n",
    ")\n",
    "\n",
    "# Plot the Concrete quantized in clear model's zoomed trend line\n",
    "axins.plot(\n",
    "    test_data,\n",
    "    concrete_predictions,\n",
    "    color=\"red\",\n",
    ")\n",
    "\n",
    "# Parametrize the zoomed figure\n",
    "x1, x2, y1, y2 = 60, 65, 0.3, 0.7\n",
    "axins.set_xlim(x1, x2)\n",
    "axins.set_ylim(y1, y2)\n",
    "axins.grid()\n",
    "ax.indicate_inset_zoom(axins, edgecolor=\"black\")\n",
    "\n",
    "display(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa8854b2",
   "metadata": {},
   "source": [
    "### Analysis\n",
    "\n",
    "In the graph above, the two trend lines seem to match perfectly. The quantization process thus seem to have a very negligible impact."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af6bc89e",
   "metadata": {},
   "source": [
    "### Now it's time to make the inference homomorphic. \n",
    "\n",
    "Compiling a model to FHE is done with a single line of code. Providing some data to the compile method is necessary because it will help the model evaluate the maximum bit size of integers actually needed during the FHE computations before running any predictions. Usually, the given data is the training data-set or a representative subset of it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fe9935bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "fhe_circuit = concrete_pr.compile(train_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3714641f",
   "metadata": {},
   "source": [
    "Then, we generate the key for the FHE circuit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4367be2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating a key for an 11-bit circuit\n"
     ]
    }
   ],
   "source": [
    "print(f\"Generating a key for an {fhe_circuit.graph.maximum_integer_bit_width()}-bit circuit\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0d435e08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Key generation time: 0.0027 seconds\n"
     ]
    }
   ],
   "source": [
    "time_begin = time.time()\n",
    "fhe_circuit.client.keygen(force=False)\n",
    "print(f\"Key generation time: {time.time() - time_begin:.4f} seconds\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "46753da7",
   "metadata": {},
   "source": [
    "Now we can test the model on the test set in FHE by simply setting `fhe` to `execute`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ca928b78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: 0.0027 seconds per sample\n"
     ]
    }
   ],
   "source": [
    "time_begin = time.time()\n",
    "concrete_predictions_fhe = concrete_pr.predict(test_data, fhe=\"execute\")\n",
    "print(f\"Execution time: {(time.time() - time_begin) / len(test_data):.4f} seconds per sample\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "357dd231",
   "metadata": {},
   "source": [
    "Similarly, let's compute its mean Poisson deviance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bf1ef97d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean Poisson deviance (Concrete FHE): 1.6743\n"
     ]
    }
   ],
   "source": [
    "concrete_fhe_score = mean_poisson_deviance(\n",
    "    y_true, concrete_predictions_fhe, sample_weight=sample_weight\n",
    ")\n",
    "\n",
    "print(f\"mean Poisson deviance (Concrete FHE): {concrete_fhe_score:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68f67b3f",
   "metadata": {},
   "source": [
    "We get the same score as the quantized model !\n",
    "\n",
    "Finally, we visually check if there are any differences with the quantized model on non-encrypted clear data by plotting the trend lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "92c7f2f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "fig, ax = plt.subplots(1, figsize=(12, 8))\n",
    "fig.patch.set_facecolor(\"white\")\n",
    "\n",
    "# Plot the scikit-learn in clear model's main trend line\n",
    "ax.plot(\n",
    "    test_data,\n",
    "    sklearn_predictions,\n",
    "    color=\"black\",\n",
    "    label=f\"scikit-learn float, d={sklearn_score:.3f}\",\n",
    ")\n",
    "\n",
    "# Plot the Concrete quantized in clear model's main trend line\n",
    "ax.plot(\n",
    "    test_data,\n",
    "    concrete_predictions,\n",
    "    color=\"red\",\n",
    "    label=f\"Concrete ML quantized, d={concrete_score:.3f}\",\n",
    ")\n",
    "\n",
    "# Plot the Concrete FHE model's main trend line\n",
    "ax.plot(\n",
    "    test_data,\n",
    "    concrete_predictions_fhe,\n",
    "    color=\"blue\",\n",
    "    label=f\"Concrete ML FHE, d={concrete_fhe_score:.3f}\",\n",
    ")\n",
    "\n",
    "# Plot the test data\n",
    "ax.scatter(df_test[\"DrivAge\"], df_test[\"Frequency\"], marker=\"o\", color=\"gray\", label=\"Test data\")\n",
    "\n",
    "# Parametrize the main figure\n",
    "ax.set_xlabel(\"Driver Age\")\n",
    "ax.set_ylim(0, 10)\n",
    "ax.set_title(\"Poisson Regression, float in clear, quantized in clear and FHE trend lines\")\n",
    "ax.set_ylabel(\"Frequency of claims\")\n",
    "ax.legend(loc=\"upper left\")\n",
    "ax.grid()\n",
    "\n",
    "# Set a zoomed-in figure\n",
    "axins = ax.inset_axes([0.5, 0.5, 0.47, 0.47])\n",
    "\n",
    "# Plot the scikit-learn in clear model's zoomed trend line\n",
    "axins.plot(\n",
    "    test_data,\n",
    "    sklearn_predictions,\n",
    "    color=\"black\",\n",
    ")\n",
    "\n",
    "# Plot the Concrete FHE model's zoomed trend line\n",
    "axins.plot(\n",
    "    test_data,\n",
    "    concrete_predictions,\n",
    "    color=\"red\",\n",
    ")\n",
    "\n",
    "# Plot the Concrete FHE model's zoomed trend line\n",
    "axins.plot(\n",
    "    test_data,\n",
    "    concrete_predictions_fhe,\n",
    "    color=\"blue\",\n",
    ")\n",
    "\n",
    "# Parametrize the zoomed figure\n",
    "x1, x2, y1, y2 = 60, 65, 0.3, 0.7\n",
    "axins.set_xlim(x1, x2)\n",
    "axins.set_ylim(y1, y2)\n",
    "axins.grid()\n",
    "ax.indicate_inset_zoom(axins, edgecolor=\"black\")\n",
    "\n",
    "display(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "932eef4f",
   "metadata": {},
   "source": [
    "We can observe that all three trend lines (from the float, quantized and FHE models) seem identical !"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14394b94",
   "metadata": {},
   "source": [
    "## A multivariate model\n",
    "\n",
    "The simple single-variable model does not achieve good results (age is not a good predictor by itself for the number of claims). Therefore, let's train a model with all of our predictor variables. \n",
    "\n",
    "In order to do so, we will need to apply some preprocessing methods to the data with the help of scikit-learn's Pipeline tool. Let's thus import the desired packages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "37bf11f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline, make_pipeline\n",
    "from sklearn.preprocessing import (\n",
    "    FunctionTransformer,\n",
    "    KBinsDiscretizer,\n",
    "    OneHotEncoder,\n",
    "    StandardScaler,\n",
    ")\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac785f8d",
   "metadata": {},
   "source": [
    "We first proceed by transforming the raw features into ones that can be given to a regression model. Thus, the categorical features are transformed using one-hot encoding while the resolution of vehicle and person ages is reduced using binning. Transforming the data this way, we end up with a total of 57 continuous features instead of the initial 11 ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c33f64d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "sklearn_sparse_arg = (\n",
    "    {\"sparse\": False} if \"1.1.\" in sklearn.__version__ else {\"sparse_output\": False}\n",
    ")\n",
    "\n",
    "log_scale_transformer = make_pipeline(FunctionTransformer(np.log, validate=False), StandardScaler())\n",
    "\n",
    "linear_model_preprocessor = ColumnTransformer(\n",
    "    [\n",
    "        (\"passthrough_numeric\", \"passthrough\", [\"BonusMalus\"]),\n",
    "        (\"binned_numeric\", KBinsDiscretizer(n_bins=10), [\"VehAge\", \"DrivAge\"]),\n",
    "        (\"log_scaled_numeric\", log_scale_transformer, [\"Density\"]),\n",
    "        (\n",
    "            \"onehot_categorical\",\n",
    "            OneHotEncoder(**sklearn_sparse_arg),\n",
    "            [\"VehBrand\", \"VehPower\", \"VehGas\", \"Region\", \"Area\"],\n",
    "        ),\n",
    "    ],\n",
    "    remainder=\"drop\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62326a26",
   "metadata": {},
   "source": [
    "Let's now train a scikit-learn model and its equivalent FHE one using Concrete ML."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "759507c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "sklearn_pr = Pipeline(\n",
    "    [\n",
    "        (\"preprocessor\", linear_model_preprocessor),\n",
    "        (\"regressor\", SklearnPoissonRegressor()),\n",
    "    ]\n",
    ")\n",
    "\n",
    "n_bits = 16\n",
    "concrete_pr = Pipeline(\n",
    "    [\n",
    "        (\"preprocessor\", linear_model_preprocessor),\n",
    "        (\"regressor\", ConcretePoissonRegressor(n_bits=n_bits)),\n",
    "    ]\n",
    ")\n",
    "\n",
    "sklearn_pr.fit(df_train, df_train[\"Frequency\"], regressor__sample_weight=df_train[\"Exposure\"])\n",
    "\n",
    "concrete_pr.fit(df_train, df_train[\"Frequency\"], regressor__sample_weight=df_train[\"Exposure\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfbd0ff1",
   "metadata": {},
   "source": [
    "Now, let's evaluate the models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0ffae598",
   "metadata": {},
   "outputs": [],
   "source": [
    "def score_estimator(estimator, df_test, fhe=\"disable\"):\n",
    "    \"\"\"Score an estimator on the test set.\"\"\"\n",
    "\n",
    "    if fhe == \"execute\":\n",
    "        time_begin = time.time()\n",
    "        y_pred = estimator.predict(df_test, fhe=\"execute\")\n",
    "        print(\n",
    "            f\"FHE execution time: {(time.time() - time_begin) / len(df_test):.4f} \"\n",
    "            \"seconds per sample\\n\"\n",
    "        )\n",
    "\n",
    "    else:\n",
    "        y_pred = estimator.predict(df_test)\n",
    "\n",
    "    y_pred = np.squeeze(y_pred)\n",
    "    y_true = df_test[\"Frequency\"]\n",
    "    sample_weight = df_test[\"Exposure\"]\n",
    "\n",
    "    # Ignore non-positive predictions, as they are invalid for the Tweedie deviance (except if\n",
    "    # power is equal to 0, making the model equivalent to a Linear Regression). We want to\n",
    "    # issue a warning if for some reason (e.g., low quantization, user error), the regressor\n",
    "    # predictions are negative.\n",
    "\n",
    "    # Find all strictly positive values\n",
    "    mask = y_pred > 0\n",
    "\n",
    "    # If any non-positive values are found, issue a warning\n",
    "    if (~mask).any():\n",
    "        n_masked, n_samples = (~mask).sum(), mask.shape[0]\n",
    "        print(\n",
    "            \"WARNING: Estimator yields invalid, non-positive predictions \"\n",
    "            f\"for {n_masked} samples out of {n_samples}. These predictions \"\n",
    "            \"are ignored when computing the Poisson deviance.\"\n",
    "        )\n",
    "\n",
    "    return mean_poisson_deviance(y_true[mask], y_pred[mask], sample_weight=sample_weight[mask])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "af364787",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scikit-learn (clear) deviance score: 1.5905\n",
      "Concrete'ML (FHE) deviance score: 1.5905\n",
      "Relative difference between scikit-learn (clear) and Concrete-ml (FHE) scores: 0.00%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sklearn_score = score_estimator(sklearn_pr, df_test)\n",
    "concrete_score = score_estimator(concrete_pr, df_test)\n",
    "\n",
    "print(f\"scikit-learn (clear) deviance score: {sklearn_score:.4f}\")\n",
    "print(f\"Concrete'ML (FHE) deviance score: {concrete_score:.4f}\")\n",
    "\n",
    "# Measure the error of the FHE quantized model with respect to the clear scikit-learn\n",
    "# float model\n",
    "score_difference = abs(concrete_score - sklearn_score) * 100 / sklearn_score\n",
    "print(\n",
    "    \"Relative difference between scikit-learn (clear) and Concrete-ml (FHE) scores:\",\n",
    "    f\"{score_difference:.2f}%\\n\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e7012b5",
   "metadata": {},
   "source": [
    "We can observe that the deviance scores are identical (with an order of magnitude of about 1e-4)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de58b9eb",
   "metadata": {},
   "source": [
    "### Test the multivariate GLM with multiple quantization bit-widths for inputs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1185a80",
   "metadata": {},
   "source": [
    "We now want to observe the impact of quantization over the models' performance. As for all linear \n",
    "models available in Concrete ML, a user has to set the `n_bits` parameter for initialization. This \n",
    "parameter can either be:\n",
    "- a dictionary composed of `op_inputs` and `op_weights` keys. These parameters are given as \n",
    "    integers representing the number of bits over which the associated data should be quantized.\n",
    "\n",
    "- an integer, representing the number of bits over which each input and weight should be quantized.\n",
    "    Default is 8.\n",
    " \n",
    "Thus, let's evaluate the influence of quantizing the inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7ab00b76",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_bits_values = list(range(2, 20))\n",
    "concrete_deviance_scores = []\n",
    "for n_bits in n_bits_values:\n",
    "    concrete_regressor = Pipeline(\n",
    "        [\n",
    "            (\"preprocessor\", linear_model_preprocessor),\n",
    "            (\"regressor\", ConcretePoissonRegressor(n_bits=n_bits)),\n",
    "        ]\n",
    "    )\n",
    "    concrete_regressor.fit(\n",
    "        df_train, df_train[\"Frequency\"], regressor__sample_weight=df_train[\"Exposure\"]\n",
    "    )\n",
    "    concrete_deviance_scores.append(score_estimator(concrete_regressor, df_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dcb5f7e",
   "metadata": {},
   "source": [
    "We then plot the Poisson deviance with respect to the quantized bit-width in order to show how performance degrades with quantization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0e3c4858",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "fig, ax = plt.subplots(1, figsize=(12, 8))\n",
    "fig.patch.set_facecolor(\"white\")\n",
    "ax.hlines(y=sklearn_score, xmax=2, xmin=19, color=\"r\", label=\"scikit-learn\")\n",
    "ax.plot(n_bits_values, concrete_deviance_scores, label=\"Concrete ML\")\n",
    "ax.set_xlabel(\"Number of bits\")\n",
    "ax.set_ylabel(\"Poisson deviance\")\n",
    "ax.set_xticks(n_bits_values)\n",
    "ax.set_xticklabels([str(k) for k in n_bits_values])\n",
    "ax.grid()\n",
    "ax.legend(loc=\"upper right\")\n",
    "display(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43e6fd06",
   "metadata": {},
   "source": [
    "### Analysis\n",
    "\n",
    "We can observe that quantizing the inputs and weights with 6 bits or less decreases the \n",
    "model's performance. The deviance score then becomes mostly stable, until exactly matching the \n",
    "scikit-learn model's one. Besides, the slight score improvement seen with `n_bits=7` most probably\n",
    "comes from some quantization artifacts that help the model generalize a bit more to the specific \n",
    "given test data-set."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7f45c8c",
   "metadata": {},
   "source": [
    "### Compile the multivariate GLM to FHE. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3e066754",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_bits = 11\n",
    "\n",
    "poisson_regressor_fhe = Pipeline(\n",
    "    [\n",
    "        (\"preprocessor\", linear_model_preprocessor),\n",
    "        (\"regressor\", ConcretePoissonRegressor(n_bits=n_bits)),\n",
    "    ]\n",
    ")\n",
    "poisson_regressor_fhe.fit(\n",
    "    df_train, df_train[\"Frequency\"], regressor__sample_weight=df_train[\"Exposure\"]\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdda875e",
   "metadata": {},
   "source": [
    "Again, with a single line of code we compile to FHE:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f89eaa07",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile needs some preprocessed data in order to run.\n",
    "df_test_processed = poisson_regressor_fhe[\"preprocessor\"].transform(df_test)\n",
    "\n",
    "# pylint: disable-next=no-member\n",
    "fhe_circuit = poisson_regressor_fhe[\"regressor\"].compile(df_test_processed)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d910de1",
   "metadata": {},
   "source": [
    "We then generate the key."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e4a24b1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating a key for an 26-bit circuit\n"
     ]
    }
   ],
   "source": [
    "print(f\"Generating a key for an {fhe_circuit.graph.maximum_integer_bit_width()}-bit circuit\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4f8fabcb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Key generation time: 0.0007 seconds\n"
     ]
    }
   ],
   "source": [
    "time_begin = time.time()\n",
    "fhe_circuit.client.keygen(force=False)\n",
    "print(f\"Key generation time: {time.time() - time_begin:.4f} seconds\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baa0667b",
   "metadata": {},
   "source": [
    "Finally, we evaluate the model on encrypted data. Be aware that running the FHE prediction can take a few seconds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f6fe2737",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FHE execution time: 0.0392 seconds per sample\n",
      "\n",
      "scikit-learn (clear) deviance score: 1.5934\n",
      "Concrete ML (FHE) deviance score: 1.5925\n",
      "Relative difference between scikit-learn (clear) and Concrete-ml (FHE) scores: 0.00%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Reducing the test set from 10000 to 1000 for faster FHE execution\n",
    "df_test = df_test[:1000]\n",
    "\n",
    "concrete_score_fhe = score_estimator(poisson_regressor_fhe, df_test, fhe=\"execute\")\n",
    "\n",
    "print(f\"scikit-learn (clear) deviance score: {score_estimator(sklearn_pr, df_test):.4f}\")\n",
    "print(f\"Concrete ML (FHE) deviance score: {concrete_score_fhe:.4f}\")\n",
    "\n",
    "# Measure the error of the FHE quantized model with respect to the clear scikit-learn\n",
    "# float model\n",
    "score_difference = abs(concrete_score - sklearn_score) * 100 / sklearn_score\n",
    "print(\n",
    "    \"Relative difference between scikit-learn (clear) and Concrete-ml (FHE) scores:\",\n",
    "    f\"{score_difference:.2f}%\\n\",\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e7659534",
   "metadata": {},
   "source": [
    "We can observe that the deviance scores are almost identical (with an order of magnitude of about 1e-3)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c18dbdd1",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "In this tutorial, we have discussed how we can use Concrete ML in order to compute a Poisson \n",
    "regression model in FHE with a few lines of code. Additionally, we have shown that quantization has \n",
    "a negligible impact on the performance, making the Concrete ML models reach a score identical to its\n",
    "scikit-learn equivalent."
   ]
  }
 ],
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